Most wearable robots such as exoskeletons and prostheses can operate with dexterity,
while wearers do not perceive them as part of their bodies. In this perspective, we contend …

Neuromorphic hardware enables fast and power-efficient neural network–based artificial
intelligence that is well suited to solving robotic tasks. Neuromorphic algorithms can be …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …

The field of Deep Learning (DL) has seen a remarkable series of developments with
increasingly accurate and robust algorithms. However, the increase in performance has …

We present a framework for gesture customization requiring minimal examples from users,
all without degrading the performance of existing gesture sets. To achieve this, we first …

There are two principle approaches for learning in artificial intelligence: error-driven global
learning and neuroscience-oriented local learning. Integrating them into one network may …

Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the
promise for scalable, low-power information processing on temporal data streams. Yet, to …

Autonomous Driving (AD) related features provide new forms of mobility that are also
beneficial for other kind of intelligent and autonomous systems like robots, smart …

We implemented two neural network based benchmark tasks on a prototype chip of the
second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and …